Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations15341
Missing cells1533
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

cloud_cover is highly overall correlated with global_radiation and 1 other fieldsHigh correlation
global_radiation is highly overall correlated with cloud_cover and 4 other fieldsHigh correlation
max_temp is highly overall correlated with global_radiation and 2 other fieldsHigh correlation
mean_temp is highly overall correlated with global_radiation and 2 other fieldsHigh correlation
min_temp is highly overall correlated with global_radiation and 2 other fieldsHigh correlation
precipitation is highly overall correlated with pressureHigh correlation
pressure is highly overall correlated with precipitationHigh correlation
sunshine is highly overall correlated with cloud_cover and 1 other fieldsHigh correlation
snow_depth has 1441 (9.4%) missing valuesMissing
snow_depth is highly skewed (γ1 = 22.58706503)Skewed
date has unique valuesUnique
cloud_cover has 381 (2.5%) zerosZeros
sunshine has 2570 (16.8%) zerosZeros
precipitation has 7963 (51.9%) zerosZeros
snow_depth has 13760 (89.7%) zerosZeros

Reproduction

Analysis started2024-09-28 11:14:06.885490
Analysis finished2024-09-28 11:14:19.308084
Duration12.42 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

date
Real number (ℝ)

UNIQUE 

Distinct15341
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19995672
Minimum19790101
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:19.428603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19790101
5-th percentile19810206
Q119890702
median20000101
Q320100702
95-th percentile20181125
Maximum20201231
Range411130
Interquartile range (IQR)210000

Descriptive statistics

Standard deviation121217.56
Coefficient of variation (CV)0.0060621899
Kurtosis-1.2013449
Mean19995672
Median Absolute Deviation (MAD)108870
Skewness7.2397371 × 10-6
Sum3.067536 × 1011
Variance1.4693696 × 1010
MonotonicityStrictly increasing
2024-09-28T14:14:19.627838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201231 1
 
< 0.1%
19790101 1
 
< 0.1%
19790102 1
 
< 0.1%
19790103 1
 
< 0.1%
19790104 1
 
< 0.1%
19790105 1
 
< 0.1%
19790106 1
 
< 0.1%
19790107 1
 
< 0.1%
19790108 1
 
< 0.1%
19790109 1
 
< 0.1%
Other values (15331) 15331
99.9%
ValueCountFrequency (%)
19790101 1
< 0.1%
19790102 1
< 0.1%
19790103 1
< 0.1%
19790104 1
< 0.1%
19790105 1
< 0.1%
19790106 1
< 0.1%
19790107 1
< 0.1%
19790108 1
< 0.1%
19790109 1
< 0.1%
19790110 1
< 0.1%
ValueCountFrequency (%)
20201231 1
< 0.1%
20201230 1
< 0.1%
20201229 1
< 0.1%
20201228 1
< 0.1%
20201227 1
< 0.1%
20201226 1
< 0.1%
20201225 1
< 0.1%
20201224 1
< 0.1%
20201223 1
< 0.1%
20201222 1
< 0.1%

cloud_cover
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.2682417
Minimum0
Maximum9
Zeros381
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:19.751892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0700721
Coefficient of variation (CV)0.39293415
Kurtosis-0.27150969
Mean5.2682417
Median Absolute Deviation (MAD)1
Skewness-0.68684171
Sum80720
Variance4.2851985
MonotonicityNot monotonic
2024-09-28T14:14:19.860373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 3191
20.8%
6 3014
19.6%
5 2329
15.2%
8 1934
12.6%
4 1834
12.0%
3 1163
 
7.6%
2 865
 
5.6%
1 609
 
4.0%
0 381
 
2.5%
9 2
 
< 0.1%
(Missing) 19
 
0.1%
ValueCountFrequency (%)
0 381
 
2.5%
1 609
 
4.0%
2 865
 
5.6%
3 1163
 
7.6%
4 1834
12.0%
5 2329
15.2%
6 3014
19.6%
7 3191
20.8%
8 1934
12.6%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 1934
12.6%
7 3191
20.8%
6 3014
19.6%
5 2329
15.2%
4 1834
12.0%
3 1163
 
7.6%
2 865
 
5.6%
1 609
 
4.0%
0 381
 
2.5%

sunshine
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3502379
Minimum0
Maximum16
Zeros2570
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:19.997931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median3.5
Q37.2
95-th percentile12.2
Maximum16
Range16
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.0283394
Coefficient of variation (CV)0.92600439
Kurtosis-0.53328495
Mean4.3502379
Median Absolute Deviation (MAD)3.2
Skewness0.68737158
Sum66737
Variance16.227518
MonotonicityNot monotonic
2024-09-28T14:14:20.175806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2570
 
16.8%
0.1 574
 
3.7%
0.2 263
 
1.7%
0.3 240
 
1.6%
0.5 181
 
1.2%
0.4 179
 
1.2%
0.7 163
 
1.1%
0.8 154
 
1.0%
0.6 154
 
1.0%
1.1 149
 
1.0%
Other values (150) 10714
69.8%
ValueCountFrequency (%)
0 2570
16.8%
0.1 574
 
3.7%
0.2 263
 
1.7%
0.3 240
 
1.6%
0.4 179
 
1.2%
0.5 181
 
1.2%
0.6 154
 
1.0%
0.7 163
 
1.1%
0.8 154
 
1.0%
0.9 141
 
0.9%
ValueCountFrequency (%)
16 1
 
< 0.1%
15.9 1
 
< 0.1%
15.7 7
< 0.1%
15.6 3
 
< 0.1%
15.5 5
< 0.1%
15.4 5
< 0.1%
15.3 7
< 0.1%
15.2 9
0.1%
15.1 12
0.1%
15 7
< 0.1%

global_radiation
Real number (ℝ)

HIGH CORRELATION 

Distinct360
Distinct (%)2.3%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean118.75695
Minimum8
Maximum402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:20.356256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q141
median95
Q3186
95-th percentile288
Maximum402
Range394
Interquartile range (IQR)145

Descriptive statistics

Standard deviation88.898272
Coefficient of variation (CV)0.74857321
Kurtosis-0.66389474
Mean118.75695
Median Absolute Deviation (MAD)64
Skewness0.65847528
Sum1819594
Variance7902.9027
MonotonicityNot monotonic
2024-09-28T14:14:20.679804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 352
 
2.3%
13 269
 
1.8%
15 169
 
1.1%
16 169
 
1.1%
17 166
 
1.1%
14 157
 
1.0%
18 138
 
0.9%
19 132
 
0.9%
21 129
 
0.8%
25 123
 
0.8%
Other values (350) 13518
88.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 1
 
< 0.1%
12 352
2.3%
13 269
1.8%
14 157
1.0%
15 169
1.1%
16 169
1.1%
17 166
1.1%
18 138
 
0.9%
19 132
 
0.9%
ValueCountFrequency (%)
402 1
< 0.1%
399 1
< 0.1%
398 1
< 0.1%
397 1
< 0.1%
395 1
< 0.1%
394 1
< 0.1%
389 1
< 0.1%
388 1
< 0.1%
386 1
< 0.1%
380 2
< 0.1%

max_temp
Real number (ℝ)

HIGH CORRELATION 

Distinct374
Distinct (%)2.4%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.388777
Minimum-6.2
Maximum37.9
Zeros5
Zeros (%)< 0.1%
Negative41
Negative (%)0.3%
Memory size120.0 KiB
2024-09-28T14:14:20.860211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2
5-th percentile5
Q110.5
median15
Q320.3
95-th percentile26.2
Maximum37.9
Range44.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.5547536
Coefficient of variation (CV)0.42594375
Kurtosis-0.50452057
Mean15.388777
Median Absolute Deviation (MAD)4.9
Skewness0.12575885
Sum235986.9
Variance42.964794
MonotonicityNot monotonic
2024-09-28T14:14:21.025332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 119
 
0.8%
12.6 113
 
0.7%
11.5 108
 
0.7%
13.9 104
 
0.7%
12.5 103
 
0.7%
12.1 101
 
0.7%
11 100
 
0.7%
12.9 100
 
0.7%
12.2 99
 
0.6%
10 99
 
0.6%
Other values (364) 14289
93.1%
ValueCountFrequency (%)
-6.2 1
< 0.1%
-4 1
< 0.1%
-3.4 1
< 0.1%
-3.3 1
< 0.1%
-3 1
< 0.1%
-2.5 1
< 0.1%
-2.1 1
< 0.1%
-1.8 2
< 0.1%
-1.7 2
< 0.1%
-1.6 1
< 0.1%
ValueCountFrequency (%)
37.9 3
< 0.1%
36.7 1
 
< 0.1%
36.5 2
< 0.1%
35.7 1
 
< 0.1%
35.5 2
< 0.1%
35.2 1
 
< 0.1%
35 2
< 0.1%
34.5 1
 
< 0.1%
34.4 1
 
< 0.1%
34.3 1
 
< 0.1%

mean_temp
Real number (ℝ)

HIGH CORRELATION 

Distinct321
Distinct (%)2.1%
Missing36
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean11.475511
Minimum-7.6
Maximum29
Zeros10
Zeros (%)0.1%
Negative225
Negative (%)1.5%
Memory size120.0 KiB
2024-09-28T14:14:21.172071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7.6
5-th percentile2.3
Q17
median11.4
Q316
95-th percentile20.5
Maximum29
Range36.6
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7297085
Coefficient of variation (CV)0.49929876
Kurtosis-0.65613586
Mean11.475511
Median Absolute Deviation (MAD)4.4
Skewness-0.01302331
Sum175632.7
Variance32.82956
MonotonicityNot monotonic
2024-09-28T14:14:21.325276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 141
 
0.9%
10.2 135
 
0.9%
8 134
 
0.9%
15 133
 
0.9%
16.6 132
 
0.9%
13.8 132
 
0.9%
6.8 131
 
0.9%
10 129
 
0.8%
8.2 129
 
0.8%
15.6 128
 
0.8%
Other values (311) 13981
91.1%
ValueCountFrequency (%)
-7.6 1
 
< 0.1%
-6.2 1
 
< 0.1%
-5.4 1
 
< 0.1%
-5.2 1
 
< 0.1%
-4.7 1
 
< 0.1%
-4.6 1
 
< 0.1%
-4.4 3
< 0.1%
-4.2 2
< 0.1%
-4.1 3
< 0.1%
-4 1
 
< 0.1%
ValueCountFrequency (%)
29 1
< 0.1%
28.8 1
< 0.1%
28.7 1
< 0.1%
28.6 1
< 0.1%
28.4 1
< 0.1%
27.8 1
< 0.1%
27.4 1
< 0.1%
27.2 2
< 0.1%
27.1 2
< 0.1%
27 1
< 0.1%

min_temp
Real number (ℝ)

HIGH CORRELATION 

Distinct296
Distinct (%)1.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.559867
Minimum-11.8
Maximum22.3
Zeros57
Zeros (%)0.4%
Negative1280
Negative (%)8.3%
Memory size120.0 KiB
2024-09-28T14:14:21.495163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-11.8
5-th percentile-1.3
Q13.5
median7.8
Q311.8
95-th percentile15.7
Maximum22.3
Range34.1
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation5.3267558
Coefficient of variation (CV)0.70460972
Kurtosis-0.64901479
Mean7.559867
Median Absolute Deviation (MAD)4.1
Skewness-0.1746867
Sum115960.8
Variance28.374327
MonotonicityNot monotonic
2024-09-28T14:14:21.662853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 125
 
0.8%
13 123
 
0.8%
7.5 119
 
0.8%
11.2 116
 
0.8%
9.5 114
 
0.7%
8.5 113
 
0.7%
6.5 112
 
0.7%
11.5 112
 
0.7%
11.9 111
 
0.7%
8.6 110
 
0.7%
Other values (286) 14184
92.5%
ValueCountFrequency (%)
-11.8 1
 
< 0.1%
-10.1 1
 
< 0.1%
-9.6 1
 
< 0.1%
-9.4 1
 
< 0.1%
-9.1 1
 
< 0.1%
-8.9 2
< 0.1%
-8.5 1
 
< 0.1%
-8.4 1
 
< 0.1%
-8.2 1
 
< 0.1%
-8 3
< 0.1%
ValueCountFrequency (%)
22.3 1
 
< 0.1%
21.7 1
 
< 0.1%
21.4 1
 
< 0.1%
21.2 2
< 0.1%
21.1 1
 
< 0.1%
20.7 4
< 0.1%
20.6 1
 
< 0.1%
20.3 1
 
< 0.1%
20.1 2
< 0.1%
20 3
< 0.1%

precipitation
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)1.7%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.6686338
Minimum0
Maximum61.8
Zeros7963
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:21.809893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.6
95-th percentile8.63
Maximum61.8
Range61.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation3.73854
Coefficient of variation (CV)2.2404796
Kurtosis31.960639
Mean1.6686338
Median Absolute Deviation (MAD)0
Skewness4.4495848
Sum25588.5
Variance13.976681
MonotonicityNot monotonic
2024-09-28T14:14:21.956601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7963
51.9%
0.2 1021
 
6.7%
0.4 461
 
3.0%
0.1 324
 
2.1%
0.6 302
 
2.0%
1 238
 
1.6%
0.8 235
 
1.5%
1.2 203
 
1.3%
1.4 177
 
1.2%
1.8 158
 
1.0%
Other values (252) 4253
27.7%
ValueCountFrequency (%)
0 7963
51.9%
0.1 324
 
2.1%
0.2 1021
 
6.7%
0.3 140
 
0.9%
0.4 461
 
3.0%
0.5 92
 
0.6%
0.6 302
 
2.0%
0.7 69
 
0.4%
0.8 235
 
1.5%
0.9 78
 
0.5%
ValueCountFrequency (%)
61.8 1
< 0.1%
59.4 1
< 0.1%
53.1 1
< 0.1%
51.6 1
< 0.1%
45.8 1
< 0.1%
43.8 1
< 0.1%
41.7 2
< 0.1%
38.4 1
< 0.1%
37.6 1
< 0.1%
36.8 1
< 0.1%

pressure
Real number (ℝ)

HIGH CORRELATION 

Distinct642
Distinct (%)4.2%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean101536.61
Minimum95960
Maximum104820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:22.126780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum95960
5-th percentile99650
Q1100920
median101620
Q3102240
95-th percentile103160
Maximum104820
Range8860
Interquartile range (IQR)1320

Descriptive statistics

Standard deviation1049.7226
Coefficient of variation (CV)0.010338366
Kurtosis0.43495344
Mean101536.61
Median Absolute Deviation (MAD)660
Skewness-0.43312457
Sum1.5572669 × 109
Variance1101917.5
MonotonicityNot monotonic
2024-09-28T14:14:22.297564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101790 79
 
0.5%
101870 78
 
0.5%
101470 77
 
0.5%
101930 74
 
0.5%
101390 72
 
0.5%
101820 72
 
0.5%
101910 72
 
0.5%
102010 71
 
0.5%
101700 71
 
0.5%
101900 71
 
0.5%
Other values (632) 14600
95.2%
ValueCountFrequency (%)
95960 1
< 0.1%
96370 1
< 0.1%
97150 1
< 0.1%
97230 1
< 0.1%
97240 1
< 0.1%
97310 2
< 0.1%
97370 1
< 0.1%
97560 1
< 0.1%
97650 1
< 0.1%
97660 1
< 0.1%
ValueCountFrequency (%)
104820 1
 
< 0.1%
104430 1
 
< 0.1%
104390 1
 
< 0.1%
104380 2
< 0.1%
104350 1
 
< 0.1%
104320 2
< 0.1%
104310 1
 
< 0.1%
104300 1
 
< 0.1%
104290 1
 
< 0.1%
104280 3
< 0.1%

snow_depth
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19
Distinct (%)0.1%
Missing1441
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean0.037985612
Minimum0
Maximum22
Zeros13760
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2024-09-28T14:14:22.432629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54563285
Coefficient of variation (CV)14.364198
Kurtosis639.40033
Mean0.037985612
Median Absolute Deviation (MAD)0
Skewness22.587065
Sum528
Variance0.2977152
MonotonicityNot monotonic
2024-09-28T14:14:22.549078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 13760
89.7%
1 49
 
0.3%
2 24
 
0.2%
4 17
 
0.1%
3 16
 
0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
11 3
 
< 0.1%
Other values (9) 11
 
0.1%
(Missing) 1441
 
9.4%
ValueCountFrequency (%)
0 13760
89.7%
1 49
 
0.3%
2 24
 
0.2%
3 16
 
0.1%
4 17
 
0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 2
< 0.1%
11 3
< 0.1%
10 2
< 0.1%
9 1
 
< 0.1%

Interactions

2024-09-28T14:14:17.618790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.170394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.417192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.508566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.608891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.746494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.839508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.008993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.330022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.509950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.760565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.294788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.517921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.611468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.718203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.842274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.951946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.124795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.452990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.615747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.877581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.398623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.622145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.712017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.823626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.937490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.055463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.239887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.576695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.712935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.009533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.534704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.763325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.834630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.947128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.058459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.175174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.356741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.722989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.835724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.136975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.654058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.882487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.959241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.076501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.189731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.293483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.484250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.862952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.957811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.243587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.755325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.983307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.066014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.188355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.290285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.398018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.593153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.985359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.060788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.365959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.862144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.097441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.176040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.311534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.398672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.512604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:14.897328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.102183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.164799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.479857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:07.965386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.205973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.292945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.428226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.505052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.634830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.010902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.212744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.292516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.599305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.070756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.318906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.405110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.541857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.607928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.747483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.123954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.318589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.412292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:18.698424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:08.164758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:09.413696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:10.509812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:11.646600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:12.720266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:13.872289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:15.227635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:16.411699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-28T14:14:17.514578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-28T14:14:22.642249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cloud_coverdateglobal_radiationmax_tempmean_tempmin_tempprecipitationpressuresnow_depthsunshine
cloud_cover1.000-0.084-0.506-0.198-0.1110.0350.347-0.2290.017-0.767
date-0.0841.0000.0050.0840.0920.0930.023-0.016-0.0380.011
global_radiation-0.5060.0051.0000.7070.6500.500-0.2780.114-0.0990.834
max_temp-0.1980.0840.7071.0000.9160.820-0.1690.082-0.1600.446
mean_temp-0.1110.0920.6500.9161.0000.958-0.096-0.012-0.1680.382
min_temp0.0350.0930.5000.8200.9581.000-0.024-0.090-0.1640.216
precipitation0.3470.023-0.278-0.169-0.096-0.0241.000-0.5080.006-0.371
pressure-0.229-0.0160.1140.082-0.012-0.090-0.5081.000-0.0170.201
snow_depth0.017-0.038-0.099-0.160-0.168-0.1640.006-0.0171.000-0.057
sunshine-0.7670.0110.8340.4460.3820.216-0.3710.201-0.0571.000

Missing values

2024-09-28T14:14:18.832670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-28T14:14:19.026096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-28T14:14:19.198633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datecloud_coversunshineglobal_radiationmax_tempmean_tempmin_tempprecipitationpressuresnow_depth
0197901012.07.052.02.3-4.1-7.50.4101900.09.0
1197901026.01.727.01.6-2.6-7.50.0102530.08.0
2197901035.00.013.01.3-2.8-7.20.0102050.04.0
3197901048.00.013.0-0.3-2.6-6.50.0100840.02.0
4197901056.02.029.05.6-0.8-1.40.0102250.01.0
5197901065.03.839.08.3-0.5-6.60.7102780.01.0
6197901078.00.013.08.51.5-5.35.2102520.00.0
7197901088.00.115.05.86.95.30.8101870.00.0
8197901094.05.850.05.23.71.67.2101170.00.0
9197901107.01.930.04.93.31.42.198700.00.0
datecloud_coversunshineglobal_radiationmax_tempmean_tempmin_tempprecipitationpressuresnow_depth
15331202012228.00.016.011.711.711.70.6100970.0NaN
15332202012237.00.023.014.012.010.11.8100550.0NaN
15333202012242.01.332.06.16.16.10.0101830.0NaN
15334202012256.03.540.04.62.60.70.0103100.0NaN
1533520201226NaN2.138.010.04.9-0.112.0101960.0NaN
15336202012271.00.932.07.57.57.62.098000.0NaN
15337202012287.03.738.03.61.1-1.30.297370.0NaN
15338202012297.00.021.04.12.61.10.098830.0NaN
15339202012306.00.422.05.62.7-0.10.0100200.0NaN
15340202012317.01.334.01.5-0.8-3.10.0100500.0NaN